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ICHNet: Intracerebral Hemorrhage (ICH) Segmentation Using Deep Learning

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11383)

Abstract

We develop a deep learning approach for automated intracerebral hemorrhage (ICH) segmentation from 3D computed tomography (CT) scans. Our model, ICHNet, evolves by integrating dilated convolution neural network (CNN) with hypercolumn features where a modest number of pixels are sampled and corresponding features from multiple layers are concatenated. Due to freedom of sampling pixels rather than image patch, this model trains within the brain region and ignores the CT background padding. This boosts the convergence time and accuracy by learning only healthy and defected brain tissues. To overcome the class imbalance problem, we sample an equal number of pixels from each class. We also incorporate 3D conditional random field (3D CRF) to smoothen the predicted segmentation as a post-processing step. ICHNet demonstrates 87.6% Dice accuracy in hemorrhage segmentation, that is comparable to radiologists.

Keywords

  • Intracerebral hemorrhage
  • Stroke
  • Deep learning
  • Convolutional neural network
  • PixelNet
  • Conditional Random Field
  • Hypercolumn

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References

  1. van Asch, C.J., Luitse, M.J., Rinkel, G.J., van der Tweel, I., Algra, A., Klijn, C.J.: Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: a systematic review and meta-analysis. Lancet Neurol. 9(2), 167–176 (2010)

    CrossRef  Google Scholar 

  2. Bansal, A., Chen, X., Russell, B., Ramanan, A.G., et al.: PixelNet: representation of the pixels, by the pixels, and for the pixels. arXiv preprint arXiv:1702.06506 (2017)

  3. Bansal, A., Russell, B., Gupta, A.: Marr revisited: 2D-3D alignment via surface normal prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5965–5974 (2016)

    Google Scholar 

  4. Cai, J., Lu, L., Xie, Y., Xing, F., Yang, L.: Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function. arXiv preprint arXiv:1707.04912 (2017)

  5. Chen, L., Bentley, P., Rueckert, D.: Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. NeuroImage: Clin. 15, 633–643 (2017)

    CrossRef  Google Scholar 

  6. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)

    CrossRef  Google Scholar 

  7. Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)

  8. Choi, Y., Kwon, Y., Lee, H., Kim, B.J., Paik, M.C., Won, J.H.: Ensemble of deep convolutional neural networks for prognosis of ischemic stroke. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds.) BrainLes 2016. LNCS, vol. 10154, pp. 231–243. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-55524-9_22

    CrossRef  Google Scholar 

  9. Grewal, M., Srivastava, M.M., Kumar, P., Varadarajan, S.: RADNET: radiologist level accuracy using deep learning for hemorrhage detection in CT scans. arXiv preprint arXiv:1710.04934 (2017)

  10. Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 447–456 (2015)

    Google Scholar 

  11. Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Analysis 35, 18–31 (2017)

    CrossRef  Google Scholar 

  12. Hwang, S., Park, S.: Accurate lung segmentation via network-wise training of convolutional networks. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 92–99. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_11

    CrossRef  Google Scholar 

  13. Islam, M., Ren, H.: Multi-modal PixelNet for brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 298–308. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_26

    CrossRef  Google Scholar 

  14. Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1175–1183. IEEE (2017)

    Google Scholar 

  15. Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)

    Google Scholar 

  16. Kalita, J., Misra, U., Vajpeyee, A., Phadke, R., Handique, A., Salwani, V.: Brain herniations in patients with intracerebral hemorrhage. Acta Neurol. Scand. 119(4), 254–260 (2009)

    CrossRef  Google Scholar 

  17. Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)

    CrossRef  Google Scholar 

  18. Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: Advances in Neural Information Processing Systems, pp. 109–117 (2011)

    Google Scholar 

  19. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional models for semantic segmentation. In: CVPR, vol. 3, p. 4 (2015)

    Google Scholar 

  20. Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)

    Google Scholar 

  21. Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1520–1528 (2015)

    Google Scholar 

  22. Saulle, M.F., Schambra, H.M.: Recovery and rehabilitation after intracerebral hemorrhage. In: Seminars in Neurology, vol. 36, p. 306. NIH Public Access (2016)

    Google Scholar 

  23. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  24. Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143–155 (2002)

    CrossRef  Google Scholar 

  25. Tran, P.V.: A fully convolutional neural network for cardiac segmentation in short-axis MRI. arXiv preprint arXiv:1604.00494 (2016)

  26. Zhao, H., Qi, X., Shen, X., Shi, J., Jia, J.: ICNet for real-time semantic segmentation on high-resolution images. arXiv preprint arXiv:1704.08545 (2017)

  27. Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2881–2890 (2017)

    Google Scholar 

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Acknowledgement

This work is supported by the Singapore Academic Research Fund under Grant R-397-000-227-112, NUSRI China Jiangsu Provincial Grant BK20150386 and BE2016077 and NMRC Bedside & Bench under grant R-397-000-245-511 awarded to Dr. Hongliang Ren.

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Islam, M., Sanghani, P., See, A.A.Q., James, M.L., King, N.K.K., Ren, H. (2019). ICHNet: Intracerebral Hemorrhage (ICH) Segmentation Using Deep Learning. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science(), vol 11383. Springer, Cham. https://doi.org/10.1007/978-3-030-11723-8_46

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  • DOI: https://doi.org/10.1007/978-3-030-11723-8_46

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